






"""simple docstring"""


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE   =   logging.get_logger(__name__)


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase   :List[Any]						=			MobileViTConfig()

						# size of the architecture
						if "mobilevit_s" in mobilevit_name:
												lowerCAmelCase   :str						=			[1_44, 1_92, 2_40]
												lowerCAmelCase   :Union[str, Any]						=			[16, 32, 64, 96, 1_28, 1_60, 6_40]
						elif "mobilevit_xs" in mobilevit_name:
												lowerCAmelCase   :int						=			[96, 1_20, 1_44]
												lowerCAmelCase   :Dict						=			[16, 32, 48, 64, 80, 96, 3_84]
						elif "mobilevit_xxs" in mobilevit_name:
												lowerCAmelCase   :Union[str, Any]						=			[64, 80, 96]
												lowerCAmelCase   :Optional[int]						=			[16, 16, 24, 48, 64, 80, 3_20]
												lowerCAmelCase   :List[str]						=			0.05
												lowerCAmelCase   :Optional[int]						=			2.0

						if mobilevit_name.startswith('deeplabv3_' ):
												lowerCAmelCase   :Union[str, Any]						=			5_12
												lowerCAmelCase   :Dict						=			16
												lowerCAmelCase   :Union[str, Any]						=			21
												lowerCAmelCase   :str						=			'pascal-voc-id2label.json'
						else:
												lowerCAmelCase   :Dict						=			10_00
												lowerCAmelCase   :Union[str, Any]						=			'imagenet-1k-id2label.json'

						lowerCAmelCase   :Optional[Any]						=			'huggingface/label-files'
						lowerCAmelCase   :Any						=			json.load(open(hf_hub_download(a__     ,      a__     ,      repo_type='dataset' )     ,      'r' ) )
						lowerCAmelCase   :Optional[int]						=			{int(a__ ): v for k, v in idalabel.items()}
						lowerCAmelCase   :Any						=			idalabel
						lowerCAmelCase   :str						=			{v: k for k, v in idalabel.items()}

						return config


def        UpperCAmelCase      (					a__     ,      a__=False ):





						'''simple docstring'''






						for i in range(1     ,      6 ):
												if F"""layer_{i}.""" in name:
																		lowerCAmelCase   :Union[str, Any]						=			name.replace(F"""layer_{i}."""     ,      F"""encoder.layer.{i - 1}.""" )

						if "conv_1." in name:
												lowerCAmelCase   :str						=			name.replace('conv_1.'     ,      'conv_stem.' )
						if ".block." in name:
												lowerCAmelCase   :str						=			name.replace('.block.'     ,      '.' )

						if "exp_1x1" in name:
												lowerCAmelCase   :int						=			name.replace('exp_1x1'     ,      'expand_1x1' )
						if "red_1x1" in name:
												lowerCAmelCase   :Union[str, Any]						=			name.replace('red_1x1'     ,      'reduce_1x1' )
						if ".local_rep.conv_3x3." in name:
												lowerCAmelCase   :Any						=			name.replace('.local_rep.conv_3x3.'     ,      '.conv_kxk.' )
						if ".local_rep.conv_1x1." in name:
												lowerCAmelCase   :Optional[Any]						=			name.replace('.local_rep.conv_1x1.'     ,      '.conv_1x1.' )
						if ".norm." in name:
												lowerCAmelCase   :Optional[Any]						=			name.replace('.norm.'     ,      '.normalization.' )
						if ".conv." in name:
												lowerCAmelCase   :Union[str, Any]						=			name.replace('.conv.'     ,      '.convolution.' )
						if ".conv_proj." in name:
												lowerCAmelCase   :Dict						=			name.replace('.conv_proj.'     ,      '.conv_projection.' )

						for i in range(0     ,      2 ):
												for j in range(0     ,      4 ):
																		if F""".{i}.{j}.""" in name:
																								lowerCAmelCase   :int						=			name.replace(F""".{i}.{j}."""     ,      F""".{i}.layer.{j}.""" )

						for i in range(2     ,      6 ):
												for j in range(0     ,      4 ):
																		if F""".{i}.{j}.""" in name:
																								lowerCAmelCase   :int						=			name.replace(F""".{i}.{j}."""     ,      F""".{i}.""" )
																								if "expand_1x1" in name:
																														lowerCAmelCase   :Optional[int]						=			name.replace('expand_1x1'     ,      'downsampling_layer.expand_1x1' )
																								if "conv_3x3" in name:
																														lowerCAmelCase   :Optional[Any]						=			name.replace('conv_3x3'     ,      'downsampling_layer.conv_3x3' )
																								if "reduce_1x1" in name:
																														lowerCAmelCase   :List[str]						=			name.replace('reduce_1x1'     ,      'downsampling_layer.reduce_1x1' )

						for i in range(2     ,      5 ):
												if F""".global_rep.{i}.weight""" in name:
																		lowerCAmelCase   :List[str]						=			name.replace(F""".global_rep.{i}.weight"""     ,      '.layernorm.weight' )
												if F""".global_rep.{i}.bias""" in name:
																		lowerCAmelCase   :Tuple						=			name.replace(F""".global_rep.{i}.bias"""     ,      '.layernorm.bias' )

						if ".global_rep." in name:
												lowerCAmelCase   :str						=			name.replace('.global_rep.'     ,      '.transformer.' )
						if ".pre_norm_mha.0." in name:
												lowerCAmelCase   :Dict						=			name.replace('.pre_norm_mha.0.'     ,      '.layernorm_before.' )
						if ".pre_norm_mha.1.out_proj." in name:
												lowerCAmelCase   :Tuple						=			name.replace('.pre_norm_mha.1.out_proj.'     ,      '.attention.output.dense.' )
						if ".pre_norm_ffn.0." in name:
												lowerCAmelCase   :Tuple						=			name.replace('.pre_norm_ffn.0.'     ,      '.layernorm_after.' )
						if ".pre_norm_ffn.1." in name:
												lowerCAmelCase   :Dict						=			name.replace('.pre_norm_ffn.1.'     ,      '.intermediate.dense.' )
						if ".pre_norm_ffn.4." in name:
												lowerCAmelCase   :List[Any]						=			name.replace('.pre_norm_ffn.4.'     ,      '.output.dense.' )
						if ".transformer." in name:
												lowerCAmelCase   :List[Any]						=			name.replace('.transformer.'     ,      '.transformer.layer.' )

						if ".aspp_layer." in name:
												lowerCAmelCase   :Tuple						=			name.replace('.aspp_layer.'     ,      '.' )
						if ".aspp_pool." in name:
												lowerCAmelCase   :Tuple						=			name.replace('.aspp_pool.'     ,      '.' )
						if "seg_head." in name:
												lowerCAmelCase   :int						=			name.replace('seg_head.'     ,      'segmentation_head.' )
						if "segmentation_head.classifier.classifier." in name:
												lowerCAmelCase   :Optional[Any]						=			name.replace('segmentation_head.classifier.classifier.'     ,      'segmentation_head.classifier.' )

						if "classifier.fc." in name:
												lowerCAmelCase   :Any						=			name.replace('classifier.fc.'     ,      'classifier.' )
						elif (not base_model) and ("segmentation_head." not in name):
												lowerCAmelCase   :List[str]						=			'mobilevit.' + name

						return name


def        UpperCAmelCase      (					a__     ,      a__     ,      a__=False ):





						'''simple docstring'''






						if base_model:
												lowerCAmelCase   :Optional[Any]						=			''
						else:
												lowerCAmelCase   :Union[str, Any]						=			'mobilevit.'

						for key in orig_state_dict.copy().keys():
												lowerCAmelCase   :str						=			orig_state_dict.pop(a__ )

												if key[:8] == "encoder.":
																		lowerCAmelCase   :List[Any]						=			key[8:]

												if "qkv" in key:
																		lowerCAmelCase   :List[str]						=			key.split('.' )
																		lowerCAmelCase   :Union[str, Any]						=			int(key_split[0][6:] ) - 1
																		lowerCAmelCase   :str						=			int(key_split[3] )
																		lowerCAmelCase   :List[Any]						=			model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}""" )
																		lowerCAmelCase   :List[Any]						=			layer.transformer.layer[transformer_num].attention.attention.all_head_size
																		lowerCAmelCase   :str						=			(
																		    F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
																		)
																		if "weight" in key:
																								lowerCAmelCase   :Optional[Any]						=			val[:dim, :]
																								lowerCAmelCase   :Tuple						=			val[dim : dim * 2, :]
																								lowerCAmelCase   :Tuple						=			val[-dim:, :]
																		else:
																								lowerCAmelCase   :Union[str, Any]						=			val[:dim]
																								lowerCAmelCase   :Dict						=			val[dim : dim * 2]
																								lowerCAmelCase   :Any						=			val[-dim:]
												else:
																		lowerCAmelCase   :Dict						=			val

						return orig_state_dict


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :Optional[int]						=			'http://images.cocodataset.org/val2017/000000039769.jpg'
						lowerCAmelCase   :Union[str, Any]						=			Image.open(requests.get(a__     ,      stream=a__ ).raw )
						return im


@torch.no_grad()
def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__=False ):





						'''simple docstring'''






						lowerCAmelCase   :Optional[int]						=			get_mobilevit_config(a__ )

						# load original state_dict
						lowerCAmelCase   :Union[str, Any]						=			torch.load(a__     ,      map_location='cpu' )

						# load 🤗 model
						if mobilevit_name.startswith('deeplabv3_' ):
												lowerCAmelCase   :Any						=			MobileViTForSemanticSegmentation(a__ ).eval()
						else:
												lowerCAmelCase   :str						=			MobileViTForImageClassification(a__ ).eval()

						lowerCAmelCase   :Any						=			convert_state_dict(a__     ,      a__ )
						model.load_state_dict(a__ )

						# Check outputs on an image, prepared by MobileViTImageProcessor
						lowerCAmelCase   :Any						=			MobileViTImageProcessor(crop_size=config.image_size     ,      size=config.image_size + 32 )
						lowerCAmelCase   :List[str]						=			image_processor(images=prepare_img()     ,      return_tensors='pt' )
						lowerCAmelCase   :Optional[Any]						=			model(**a__ )
						lowerCAmelCase   :List[Any]						=			outputs.logits

						if mobilevit_name.startswith('deeplabv3_' ):
												assert logits.shape == (1, 21, 32, 32)

												if mobilevit_name == "deeplabv3_mobilevit_s":
																		lowerCAmelCase   :List[str]						=			torch.tensor(
																		    [
																		        [[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
																		        [[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
																		        [[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
																		    ] )
												elif mobilevit_name == "deeplabv3_mobilevit_xs":
																		lowerCAmelCase   :int						=			torch.tensor(
																		    [
																		        [[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
																		        [[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
																		        [[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
																		    ] )
												elif mobilevit_name == "deeplabv3_mobilevit_xxs":
																		lowerCAmelCase   :Dict						=			torch.tensor(
																		    [
																		        [[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
																		        [[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
																		        [[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
																		    ] )
												else:
																		raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )

												assert torch.allclose(logits[0, :3, :3, :3]     ,      a__     ,      atol=1e-4 )
						else:
												assert logits.shape == (1, 10_00)

												if mobilevit_name == "mobilevit_s":
																		lowerCAmelCase   :Dict						=			torch.tensor([-0.9_866, 0.2_392, -1.1_241] )
												elif mobilevit_name == "mobilevit_xs":
																		lowerCAmelCase   :Optional[int]						=			torch.tensor([-2.4_761, -0.9_399, -1.9_587] )
												elif mobilevit_name == "mobilevit_xxs":
																		lowerCAmelCase   :List[str]						=			torch.tensor([-1.9_364, -1.2_327, -0.4_653] )
												else:
																		raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )

												assert torch.allclose(logits[0, :3]     ,      a__     ,      atol=1e-4 )

						Path(a__ ).mkdir(exist_ok=a__ )
						print(F"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}""" )
						model.save_pretrained(a__ )
						print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
						image_processor.save_pretrained(a__ )

						if push_to_hub:
												lowerCAmelCase   :Tuple						=			{
												    'mobilevit_s': 'mobilevit-small',
												    'mobilevit_xs': 'mobilevit-x-small',
												    'mobilevit_xxs': 'mobilevit-xx-small',
												    'deeplabv3_mobilevit_s': 'deeplabv3-mobilevit-small',
												    'deeplabv3_mobilevit_xs': 'deeplabv3-mobilevit-x-small',
												    'deeplabv3_mobilevit_xxs': 'deeplabv3-mobilevit-xx-small',
												}

												print('Pushing to the hub...' )
												lowerCAmelCase   :Dict						=			model_mapping[mobilevit_name]
												image_processor.push_to_hub(a__     ,      organization='apple' )
												model.push_to_hub(a__     ,      organization='apple' )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--mobilevit_name',
		    default='mobilevit_s',
		    type=str,
		    help=(
		        'Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','
		        ' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'
		    ),
		)
		parser.add_argument(
		    '--checkpoint_path', required=True, type=str, help='Path to the original state dict (.pt file).'
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
		)
		parser.add_argument(
		    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
		)

		__SCREAMING_SNAKE_CASE   =   parser.parse_args()
		convert_movilevit_checkpoint(
		    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)







"""simple docstring"""


import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey




class 						__UpperCamelCase     :
					def __init__(							self							:	Union[str, Any]     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	int     , UpperCAmelCase							:	int   )	->       Tuple:
											if dst_width < 0 or dst_height < 0:
																	raise ValueError('Destination width/height should be > 0'   )

											lowerCAmelCase   :List[Any]						=			img
											lowerCAmelCase   :Optional[int]						=			img.shape[1]
											lowerCAmelCase   :Union[str, Any]						=			img.shape[0]
											lowerCAmelCase   :Optional[int]						=			dst_width
											lowerCAmelCase   :Optional[int]						=			dst_height

											lowerCAmelCase   :Any						=			self.src_w / self.dst_w
											lowerCAmelCase   :Dict						=			self.src_h / self.dst_h

											lowerCAmelCase   :int						=			(
											    np.ones((self.dst_h, self.dst_w, 3)     , np.uinta   ) * 255
											)
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       List[str]:
											for i in range(self.dst_h   ):
																	for j in range(self.dst_w   ):
																							lowerCAmelCase   :Optional[Any]						=			self.img[self.get_y(UpperCAmelCase   )][self.get_x(UpperCAmelCase   )]
					def        UpperCAmelCase__ (							self							:	Optional[int]     , UpperCAmelCase							:	int   )	->       int:
											return int(self.ratio_x * x   )
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	int   )	->       int:
											return int(self.ratio_y * y   )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE   =   8_00, 6_00
		__SCREAMING_SNAKE_CASE   =   imread('image_data/lena.jpg', 1)
		__SCREAMING_SNAKE_CASE   =   NearestNeighbour(im, dst_w, dst_h)
		n.process()

		imshow(
		    F"""Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}""", n.output
		)
		waitKey(0)
		destroyAllWindows()